import functools
import numpy
import torch.nn as nn
from olympus.utils import info
# All models here are assumed to accept RGB input, thus 3 input channels.
# Checkpoints of models pre-trained on Imagenet.
# NOT SUPPORTED YET
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
[docs]def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
[docs]class BasicBlock(nn.Module):
"""See :class`.ResNet` for license and references`"""
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
[docs] def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
[docs]class Bottleneck(nn.Module):
"""See :class`.ResNet` for license and references`"""
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
[docs] def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
[docs]class ResNet(nn.Module):
"""A residual neural network (ResNet) is an artificial neural network (ANN) of a kind
that builds on constructs known from pyramidal cells in the cerebral cortex.
Residual neural networks do this by utilizing skip connections, or short-cuts to jump over some layers.
Typical ResNet models are implemented with double- or triple- layer skips that contain nonlinearities (ReLU) and
batch normalization in between.
An additional weight matrix may be used to learn the skip weights; these models are known as HighwayNets.
Models with several parallel skips are referred to as DenseNets.
In the context of residual neural networks, a non-residual network may be described as a plain network.
More on `wikipedia <https://en.wikipedia.org/wiki/Residual_neural_network>`_.
Paper available on `arxiv <https://arxiv.org/abs/1512.03385>`_.
Original source `github <https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py>`_.
Attributes
----------
input_size: (1, 28, 28), (3, 32, 32), (3, 64, 64)
References
----------
.. [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
"Deep Residual Learning for Image Recognition", Dec 2015
Notes
-----
MIT License
Copyright (c) 2017 liukuang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
def __init__(self, block, layers, input_size, conv, maxpool, avgpool, num_classes):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(input_size[0], 64, **conv, bias=False)
# For ImageNet
# self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
# bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
if maxpool:
# For ImageNet
# self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.maxpool = nn.MaxPool2d(**maxpool)
else:
self.maxpool = None
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
if avgpool:
self.avgpool = nn.AvgPool2d(**avgpool)
else:
self.avgpool = None
self.fc = nn.Linear(512 * block.expansion, num_classes)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
# if zero_init_residual:
# for m in self.modules():
# if isinstance(m, Bottleneck):
# nn.init.constant_(m.bn3.weight, 0)
# elif isinstance(m, BasicBlock):
# nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
[docs] def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
if self.maxpool:
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
if self.avgpool:
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
[docs]def build(block, cfg, input_size, output_size):
if not isinstance(output_size, int):
output_size = numpy.product(output_size)
if input_size == (1, 28, 28):
info('Using PreActResNet architecture for MNIST')
conv = {'kernel_size': 3, 'stride': 1, 'padding': 1}
avgpool = {'kernel_size': 4}
maxpool = {}
elif input_size == (3, 32, 32):
info('Using PreActResNet architecture for CIFAR10/100')
conv = {'kernel_size': 3, 'stride': 1, 'padding': 1}
avgpool = {'kernel_size': 4}
maxpool = {}
elif input_size == (3, 64, 64):
info('Using PreActResNet architecture for TinyImageNet')
conv = {'kernel_size': 7, 'stride': 2, 'padding': 3}
avgpool = {'kernel_size': 2}
maxpool = {'kernel_size': 3, 'stride': 2, 'padding': 1}
# Add Resnet for ImageNet (3, 224, 224)!
model = ResNet(
block,
cfg,
input_size=input_size,
conv=conv,
maxpool=maxpool,
avgpool=avgpool,
num_classes=output_size
)
return model
builders = {
'resnet18': functools.partial(build, block=BasicBlock, cfg=[2, 2, 2, 2]),
'resnet34': functools.partial(build, block=BasicBlock, cfg=[3, 4, 6, 3]),
'resnet50': functools.partial(build, block=Bottleneck, cfg=[3, 4, 6, 3]),
'resnet101': functools.partial(build, block=Bottleneck, cfg=[3, 4, 23, 3]),
'resnet152': functools.partial(build, block=Bottleneck, cfg=[3, 8, 36, 3])
}